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What it’s like to be the highest-ranking woman in Major League Baseball

When Kim Ng, AB’90, started working for the Chicago White Sox in 1990, entering statistical information about players into a computer was a labor-intensive process and “still a bit of a novelty,” she said. She knew analytics would be a part of baseball’s future, “but I didn’t quite understand how the smallest piece of data was going to factor into decisions at game time and on the talent-evaluation level.”

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Ng was both a witness to and participant in the game’s data revolution, using analytics to inform scouting and contract negotiations as she rose from White Sox intern to assistant general manager of the Yankees and the Dodgers.

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Her career has been quietly revolutionary in other ways too. As senior vice president for baseball operations, Ng is the highest-ranking woman in Major League Baseball. She’s been in contention for several general manager positions and would, if hired, be the first female GM in baseball history.

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Ng isn’t holding her breath. With only 30 available positions and scant openings each year, any one person has a low chance of becoming a GM. “Those odds are not real good, are they?” she said.

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Though she’s ambivalent about the first woman GM speculation that shadows her, Ng is open about discussing her experience as a woman in sports. A softball standout and public policy major at UChicago, she wrote her senior thesis on Title IX, the law protecting people from discrimination based on sex in federally funded education programs. “I wanted to do it on a topic that I was passionate about,” she said. That piece of legislation, and the women’s movement in general, “explained why I had a lot of the opportunities that I did, and how much work we still had to do.”

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In comments edited and condensed below, Ng told UChicago Magazine about the downsides of being a GM, her approach to scouting, and how watching baseball changes when you work in the industry.

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What made you first fall in love with baseball?

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My dad was a big sports nut, so I grew up playing and watching a lot of different sports. I lived in Queens until I was 12. The Mets were right there, but I was actually a big Yankees fan, because in the late ’70s, the Yankees were such a great team. I grew up with all the greats—Thurman Munson, Ron Guidry, Reggie Jackson. I think the pace of the game and the nuance of the game were the things that really drew me to it.

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Are you able to relax and enjoy watching a game, or does it always feel like work?

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Now that I’m with MLB, I can relax a bit. When you’re with a club, you are so focused on what your team needs to do, what your division rivals are doing, who’s on the trading block, and who’s going to be a free agent, that you’re locked in.

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It was fun when the team was winning. That’s always fun.

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What happens to your childhood fandom when you work in the industry?

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We all grew up with our favorite teams. I think it does dissipate. As I’ve been in this industry longer and longer, I tend to root for people—the friends, the colleagues. You root for great players or up-and-coming players, a player you saw something in or a player you might have scouted. Your love is not necessarily tied to your childhood team as much as it is to the people you grew up with in the industry.

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You’ve worked in scouting and player development. How do you balance statistics and an intuitive sense of the player?

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It’s an interesting time in our game. I think you’re seeing that decisions that have been made leaning way heavily toward numbers don’t always work out either.

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I really try to look at both. I think it depends on what area you’re looking at. Game decisions are very different than signing free agents. With game decisions, you have a lot more data that you can go on because of sample size. How many pitches does a player see over the course of a season? A ton.

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So am I just looking at numbers in a season? No. You need to dive deeper. You have advance scouts who sit and watch the teams that you’re going to face. They know exactly how these players respond in certain situations that you might not have cataloged into your system. There are things that [advance scouts] pick up that you might not have thought about.

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I’ve always taken the approach that numbers should guide you and hopefully they’ll prevent you from making big mistakes. They should always make you question the decision you arrived at or the decision that you’re close to arriving at. Same with scouts—scouts should always make you question the numbers.

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Does the speculation about whether you will be the first female GM bother you?

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Yeah. I’m not sure if anybody asks the guys that. The idea that this is all sitting on my shoulders—it’s a lot of pressure. It’s hard. But I think someone’s going to have to do it.

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At the end of the day, if this doesn’t happen, I’m not going to see it as, “My career was a failure.” That might be other peoples’ take, but that’s not mine. I know how hard it is. I know about all the guys who didn’t even get an interview who probably should have had an interview. I’ve been very fortunate. I’ve worked extraordinarily hard to get where I am. If I don’t end up becoming a general manager, that’s just the luck of the draw. I’ve had a great career regardless.

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Leaving aside the question of gender, do you want to be a general manager? Does the job itself appeal to you?

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It does. It does. In becoming a general manager, I think the one thing that you have to know, though, is that you’re never walking into a great situation. Either they finished last, or they made it to the playoffs and it just wasn’t far enough, or their payroll is through the roof and their farm system is depleted, or there are lots of difficulties surrounding their personnel. The issues go on and on. It is the ultimate challenge in this industry.

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Everything else in your life gets put on hold for the length of your contract. It’s all-consuming, especially now. The job has changed so much, with the internet and the information available to you. Social media has changed it quite a bit. Everyone has their opinions about the job you’re doing.

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I just gave a speech to some kids, and I was trying to explain to them the concept of a general manager. I said, “When your team loses the World Series, that’s the first person you’re going to blame.” But it would be a great honor and a tremendous challenge.

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Major league fields are notorious for their lack of uniformity. Do you feel there should be greater uniformity in ballparks, or do you like the quirkiness we have now?

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I do like the quirkiness. That’s my own personal opinion. It’s fun when you’re thinking about constructing a team, how to build a team around your park’s quirkiness—but knowing full well the market may dictate that you can’t build your team the way you want and you’re going to have to live with it.

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Do you have any opinions about the eternal debate over pace of play?

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[laughs] I do have opinions about that, but they’d have to be off the record.

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—Article originally appeared in UChicago Magazine

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UChicago Law School student earns Rhodes Scholarship

University of Chicago Law School student Yali Peng, LLM’17, has won a Rhodes Scholarship to study at the University of Oxford next fall. She will pursue a doctorate in either criminology or socio-legal studies and hopes to examine sentencing structures and criminal behavior with a focus on how the system affects people from marginalized communities.

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“Namely I hope to answer two questions: What kind of sentencing can better match our retribution and deterrence goals? How we eliminate those factors systematically driving people to be criminals in prison system?” said Peng, who is currently part of UChicago’s JSD program, aimed at international lawyers.

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A resident of Guangzhou, China, was among four Rhodes Scholars from China this year. She is the 53rd student from the University of Chicago to receive the award, a group that includes Joshua Pickar, JD’17, who was named a Rhodes scholar in 2016.

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Before entering the Law School, Peng earned a bachelor of laws degree from Tsinghua University, where she wrote an award-winning dissertation that examined the factors influencing the sentencing of larceny based on linear regression models. She was an editorial board member of Tsinghua China Law Review and clerked at the Supreme People’s Court of China.

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Peng is now in the second year of her JSD program, writing a dissertation that will focus on the extralegal factors influencing Chinese sentencing.

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“The Rhodes Scholarship is a tremendous honor, and one that underscores the extraordinary intellectual contributions that our LLM and JSD students make to the Law School community,” said Dean Thomas J. Miles, the Clifton R. Musser Professor of Law and Economics. “Yali’s work shows great promise, and we are delighted to see her recognized in this way.”  

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Human stories inspire legal career

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Peng said her interest in criminal behavior has grown from observing the human stories behind crimes.

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“I know a prostitute who sold her body to pay her daughter’s tuition in a prestigious private school as her husband’s wage is not enough,” Peng said. “I have also seen kids who were left behind [when their parents left] to earn money as migrant workers in big cities. Their grandparents rarely disciplined them, and they finally become local gangsters. I felt each criminal may have his or her own story, and there may be systemic forces driving people to become criminals in such a stratified society.”

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Ultimately, Peng said she hopes her work will lead to better treatment and improved opportunities for rehabilitation in criminal justice systems.

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“Yali is deeply committed both to the scholarly enterprise and to applying the insights of academic research in making a contribution to improving the lives of ordinary people in societies around the world,” said Dhammika Dharmapala, the Julius Kreeger Professor of Law and one of Peng’s recommenders.

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In addition to Dharmapala, Peng said she has received support from Tom Ginsburg, the Leo Spitz Professor of International Law; and Deputy Dean Richard McAdams, the Bernard D. Meltzer Professor of Law and the lead adviser on her JSD work.

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McAdams called Peng’s work “highly promising” and praised her contributions in class.

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“In my seminar on American policing, it was fascinating to have the comparative perspective of a student from China,” McAdams said. “Yali often made comments about Chinese police that reframed the usual American debates about policing and made us see the issues in a fresh way.”

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Peng said her work at the Law School offered her insight on the American criminal justice system.

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“I am super grateful to the education I received at UChicago Law, which gave me an insightful understanding of American society,” Peng said. “It is such a great honor that I can join a good community of Rhodes Scholars to find like minds to promote criminal justice.”

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—Story first appeared on the University of Chicago Law School website.

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Courtenay Wright, particle physicist and D-Day witness, 1923-2018

Prof. Emeritus Courtenay Wright, a gifted teacher, World War II veteran and particle physicist who changed our understanding of the structure of protons and neutrons, died Nov. 22 in Chicago. He was 95.

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During a career that spanned more than 50 years at the University of Chicago, Wright worked with scientists including Nobel laureate Enrico Fermi on particle accelerator research that provided some of the foundational measurements of the properties of quarks—the fundamental building blocks of matter.

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During World War II, Wright decoded messages as a radar officer in the Royal Navy and was among the first to know about the launch of D-Day. He was aboard the HMS Apollo when it ran aground carrying Allied Supreme Commander General Dwight D. Eisenhower to visit the beaches of Normandy on June 7, 1944—the day after D-Day.

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“I could talk about Courtenay for days,” said crime novelist Sara Paretsky, AM’69, MBA’77, PhD’77, to whom Wright was married for 42 years. “I loved his zest for life and for taking risks, his meticulous attention to detail in his work—or in reviewing my own—but above all, a deep-rooted empathy and compassion that made him open to all people, all experiences. I will never stop missing him.”

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After the war, Wright earned his doctorate from the University of California-Berkeley, studying with Manhattan Project group leader and future Nobel laureate Emilio Segrè. Not long afterward, Fermi, who helped build the world's first nuclear reactor at UChicago, recruited Wright to the University of Chicago.

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In the late ‘40s and early ‘50s, Wright studied physics processes related to nuclear fission and early nuclear reactors. The Chicago synchrocyclotron, a type of particle accelerator, became operational in 1951 under the direction of Fermi, and Wright performed experiments with the accelerator to better understand the scattering of protons and the symmetries they obey.

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“Wright made an incredible impact on the field of high-energy physics,” said Young-Kee Kim, the Louis Block Distinguished Service Professor and chair of the Department of Physics. “Modern experiments involving proton and neutron collisions rely on the knowledge of quark structure function that Wright and his contemporaries first established.”

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Hands-on engineering

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In 1971, Fermilab approved Wright’s “Experiment 98,” which aimed to study the structure of the proton using muons as a probe with the help of an analyzing magnet from the Chicago synchrocyclotron. Wright helped orchestrate the move of the 2,500-ton magnet 45 miles from the University campus to Fermilab and built the detector for these experiments.

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“His muon scattering experiments at Fermilab helped us understand the behavior of the quarks in protons and were the precursors to experiments we now do with CERN’s Large Hadron Collider,” said Prof. Emeritus James Pilcher.

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Wright also studied the rare decays of muons using the linear accelerator at Los Alamos National Laboratory in New Mexico. According to physics colleague Henry Frisch, Wright was a hands-on engineer, building trigger electronics for this experiment himself.

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“Courtenay was a beacon of thoughtfulness and consideration,” said Frisch, a professor of physics. “If a piece of equipment wasn’t working, you could ask him to take a look, and he always knew what was wrong.”

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Wright was involved in politics throughout his life: He was a member of JASON, a group of elite scientific advisers to the Pentagon during the Cold War, with whom he co-authored an analysis warning the U.S. not to use nuclear weapons in Vietnam.

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He also once drew a bid of $1,000 at an American Civil Liberties Union charity auction to explain general relativity to the bidder—a testament to his dedication to teaching and explaining physics clearly.

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Courtenay also carried on the UChicago physics tradition, begun by Albert Michelson, of playing pool in the Quadrangle Club. Under the aegis of Willie Zachariasen, Wright and Albert Crewe brought the game to new heights; two weeks before his death, Wright ran the table.

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He is survived by his wife as well as three children and one grandchild.

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How algorithms can create inequality in health care, and how to fix it

Machine-learning algorithms and artificial intelligence software help organizations analyze large amounts of data to improve decision-making, and these tools are increasingly used in hospitals to guide treatment decisions and improve efficiency. The algorithms “learn” by identifying patterns in data collected over many years. So, what happens when the data being analyzed reflects historical bias against vulnerable populations? Is it possible for these algorithms to promote further bias, leading to inequality in health care?

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Marshall Chin, the Richard Parrillo Family Professor of Healthcare Ethics at the University of Chicago Medicine, is working to ensure equity across all areas of the healthcare system, including data analysis. He has worked for three decades to examine and develop solutions addressing health disparities. Chin recently teamed up with a group of data scientists from Google to write an article in the Annals of Internal Medicine that discusses how health care providers can make these powerful new algorithms fairer and more equitable. We spoke to him about the use of machine learning in health care, and how doctors and patients can build fairness into every step of the decision-making process.

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How widely are these kinds of algorithms used in health care now?

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It varies across different settings, but they’re increasingly being used for clinical care, like reading X-rays and images to diagnose conditions like eye disease or skin cancer. They’re also being used from a business perspective to analyze medical records and insurance claims to increase the efficiency of the organization and lower costs.

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Do you think people realize how software and algorithms are used to determine their care?

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The phrase “big data” is popular because there's so much data collected on all of us, whether it's health data or on the internet. Big data are a powerful tool, but we need to clearly and explicitly discuss the ethical implications of how software can analyze and use big data. Those issues are still hidden to most people.

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What’s an example of how algorithms can create inequality in health care?

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I’ll give you an example we included in our article. There is an outstanding data analytics group at the University of Chicago Medicine, and one of the things they do is create algorithms to analyze data in the electronic medical records. One of the projects they’re working on is to help decrease the length of stay for patients, because it's in everyone’s best interest to have patients go home as soon as they're ready to leave. The thought was if we can identify patients who are most likely to be discharged early, we can assign a case manager to make sure there are no further blockages or barriers that could prevent them from leaving the hospital in a timely manner.

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The data analytics group initially developed the algorithms based on clinical data, and then they found that adding the zip code where the patient lives improved the accuracy of the model identifying those people who would have shorter lengths of stay. The problem is when you add a zip code, if you live in a poor neighborhood or a predominantly African-American neighborhood, you were more likely to have the longer length of stay. So, the algorithm would have led to the paradoxical result of the hospital providing additional case management resources to a predominantly white, more educated, more affluent population to get them out of the hospital earlier, instead of to a more socially at-risk population who really should be the ones that receive more help.

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What happened after they discovered this?

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To their credit, when the data analysis team saw this, they were horrified by the implications of what would've happened. They ended up working with James Williams, the director of Diversity, Inclusion and Equity, and our Diversity and Equity Committee at UCM. They became champions of developing formal systems to make sure these equity issues are explicitly thought about as they develop algorithms, as well as how they could proactively use machine learning to improve equity.

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So, with that local example, it became clear that this is not just an abstract, theoretical issue. It's happening here and probably a lot of other places under the radar. I think a lot of health care organizations aren't intentionally trying to do things that are going to worsen disparities, but clearly there are a lot of unintentional bad things that can happen.  Probably very few organizations are proactively using these tools to improve outcomes for everyone.

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How does inequality creep into algorithms? Is it because the input data aren’t representative of different groups of people? Or does it reflect historical bias?

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You might think of three buckets that cause the problems. One is the data itself. The second one is the algorithms, and the third is how the algorithms are used.

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You mentioned that the data may be biased. For example, poor populations can have more scattered, fragmented care. They may come here for some hospital admissions and go to Stroger Cook County Hospital for something else, as opposed to some of the more affluent patients who have a continuous source of care here. So if you're building algorithms based upon only UCM data, you'd have a more complete data set for the more affluent patients. It's likely then that whatever predictions you develop on the incomplete data aren't as accurate.

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There’s also the issue of perpetuating historical biases. For example, racial and ethnic minority patients can present differently than the textbook, psychiatric definitions of mental illness. So, if you're using flawed criteria to begin with, you're perpetuating incorrect diagnoses. Another example would be something like coronary artery disease, where women tend to be under diagnosed compared to men. If you're using criteria that under diagnose women, you may be building into your formula a perpetually biased underdiagnosis of women.

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How do you prevent these unfair outcomes from happening?

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Think back to those three buckets of the data, the formulas themselves and how the formulas are used. First, be cognizant that data could be a problem. Are you working with valid data sets? Do you have incomplete data for the at-risk population? Are you using data based upon faulty diagnoses and faulty labels? That’s an important first step.

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The second step would be examining how the models are actually developed. Here, there are technical ways to design the algorithms to advance specific principles of ethical justice. You can create algorithms that will ensure equal outcomes across two populations, and you can make sure that the technical performance of the model is fair. So, if there’s a problem where algorithms are under diagnosing African Americans for some condition, you can alter the parameters of formulas to make them more accurate.

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Another way to promote justice is to adjust formulas so you have equal allocation of resources. The previous example about assigning case managers to help people go home from the hospital sooner is a good example. You can alter the thresholds for who qualifies in these formulas to equalize the allocation of actual resources to different groups.

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How do you know it’s working?

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You can do your best trying to develop a good formula, but you still have to monitor what happens in real life, the third bucket for preventing unfair outcomes. That involves monitoring the data for inequalities and also talking to the health care providers, the patients and the administrators to determine if they see any fairness problems. One thing we recommend is to have patients at the table as we design these algorithms that will ultimately affect their lives.

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Software reflects the people who write it, so it’s important to have these fairness issues in mind. At every step of the way, we should check if the algorithm is going to lead to an unfair result. Which bad things unintentionally could happen and how can we proactively bake in ways to benefit everyone? And that requires careful attention to each step: picking the data, developing the formula, and then deploying the algorithm and monitoring how it is used.

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We must have equity and improving the health of everyone as explicit goals and then build the systems toward those goals. We must build in specific steps where there's a chance for self-reflection: Is what we're doing advancing equity for everyone, or have we unintentionally worsened things?

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—Article originally appeared on The Forefront

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Kids benefit when parents overcome math anxiety

Parents who are anxious about math can still help their children learn the subject when given the right tools, according to research from current and former University of Chicago scholars.

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A new study published in the December issue of the Journal of Experimental Psychology found that math-anxious parents who used an app called Bedtime Math saw improvement in their children’s math achievement—even years after the families stopped using the app. Exposure to the math app also changed parents’ attitudes about math for the better.

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Following 587 students from 40 classrooms across the Chicagoland area, the researchers tracked how children who used the interactive math app compared to those in a control group who used a similar app focused on reading comprehension. By monitoring the students’ progress from first through third grades, the team discovered that using the math app closed the achievement gap between children of math-anxious parents and their peers.

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That held true even after three years, when most families had stopped using the app consistently.

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Those results suggest that when families used the app together, parents disassociated their own math anxiety from what was possible for their children in terms of math achievement. Because Bedtime Math frames math problems through light-hearted themes like foot size, dogs or Halloween—and provides answers with a single click—it may represent a low-stress way for parents to approach a subject that they view as intimidating.

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“Before the intervention, the higher math-anxious parents had lower expectations for their children’s math success,” said Susan C. Levine, the Rebecca Anne Boylan Professor in Education and Society at UChicago. “They also valued math less for their children. Importantly, using the math app helped cut the link between parents’ math anxiety and their lower values and expectations about math for their children, and this helped explain the positive effect of the math app on children's long-term math achievement.”

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Levine, an expert on mathematics development in young children, collaborated on this research with Barnard College President Sian Beilock, a UChicago faculty member from 2005 to 2017.

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“Children with parents who are fearful of math learn less math across the school year,” said Beilock, who studies the pressures children face in school. “The power behind the parent-math app intervention is that it helped change parents’ own attitudes about math—how important parents thought it was for their child to succeed in math, for example. This change in the parents’ attitudes translated to children’s higher overall math achievement.”

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For decades, scholars at UChicago have conducted innovative early childhood research, including fundamental contributions to the understanding of cognitive development. In a prior large field study, Beilock, Levine and UChicago graduate students and postdocs found that when parents are more math anxious, their children learn significantly less math over the school year—but only if their math-anxious parents provided frequent help with homework.

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That connection between parents’ own math anxiety and their child’s poor math performance prompted the research team to investigate potential solutions. The scholars hypothesize that the app’s success may be tied to how it fosters positive interactions between children and parents who might otherwise shy away from math.

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The app may represent a low-stress way for parents to approach a subject that they view as intimidating, helping them disassociate their own math anxiety from their children's potential in math.

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“It makes math accessible and it makes it fun,” said UChicago PhD candidate Marjorie Schaeffer, a study lead author who has been involved with the larger project since its inception. “They’re engaging story problems that don’t look like worksheets. The app is unique because families need to do it with their children. It’s not an app you can hand your child and walk away. It’s really about that parent-child interaction.”

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Added co-lead author Christopher Rozek, a former UChicago postdoctoral scholar now at Stanford University: “The app provides a structure that makes math easier to do at home. It’s not evaluative. It gives you the answer, and there are no due dates.”

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Children with parents who fear math learned less during first through third grades, but this wasn’t the case when families had been randomized into the math app group. For families in the reading app control group, researchers found a negative relation between parents’ math anxiety and children’s math achievement.

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By the end of third grade, children of higher math-anxious parents who were in the control group lagged behind children of lower math anxious parents in this group, learning the equivalent of approximately five fewer months of math—more than half a school year. In contrast, for those families randomized in the math app group, no such gap in children’s math achievement was found.

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The researchers are hopeful that the study’s long-term results could point to other avenues for families to improve math achievement.

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“We hope this tells schools and teachers that, given proper support, all parents can help their children with math,” said Talia Berkowitz, a UChicago postdoctoral scholar and study co-author. “Providing anxious or uneasy parents with a little extra guidance and structure can go a long way toward improving children’s math outcomes.”

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This work was funded by an Overdeck Family Foundation grant to Levine and Beilock at the University of Chicago. The chair, Laura Overdeck, established the Bedtime Math Foundation, a nonprofit, 501(c)(3) organization that produces the Bedtime Math App. None of the study’s authors have a financial interest in Bedtime Math.

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Citation: “Disassociating the Relation Between Parents’ Math Anxiety and Children’s Math Achievement: Long-term Effects of a Math App Intervention,” M. Schaeffer, C. Rozek, et al. Journal of Experimental Psychology, December 2018. Doi: 10.1037/xge0000490

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Funding: Overdeck Family Foundation

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